1 Chapter 1: Introduction

Team A are the following members: Amal Alqahtani, Jiaxiang Peng, Naureen Elahi, and Xinya Mu. You may find our work over on GitHub.

Coronavirus disease-19 (COVID-19) has spread rapidly around the world, creating unprecedented damage the world was not ready for. To date, the CDC states there are a total of 4,542, 579 cases and 152, 870 deaths in the United States (Cases in U.S, 2020). Many risk factors have been hypothesized to affect the case and death rates from the virus. We felt that a relevant discussion to have would be What are the most regions with the highest number of deaths? What can we say about patient demographics? Is race considered a significant risk factor for increased COVID-19 incidence in the United States?’ Are there any general trends amongst underlying health conditions? These questions are all suited to Exploratory Data Analysis (EDA), and with these questions in mind, we want to see if we could find data on COVID-19 that would be readily available for us to analyze. Eventually, our question morphed into the following: What are the factors (i.e. patient demographics, social determinants of health, environmental variables, underlying health conditions, country of origin) affecting COVID-19 numbers of cases and death rate among different geographical locations in US?

We were able to find a dataset called Covid-19-Dataset on Github over here: https://github.com/johndurbin93/Covid-19-Dataset. This dataset includes COVID-19 confirmed case number and death number through April 14, 2020 which were obtained for each U.S. county from the Center for Systems Science and Engineering (CSSE) Coronavirus Resource Center at Johns Hopkins University. Race demographics for counties was obtained from the County Health Rankings and Roadmaps Program database. Daily temperature data for counties was obtained from the National Oceanic and Atmospheric Administration. This data was compiled by a group of reserchers.

The report is organized as follows:

  1. Description of the Data (explanation of the dataset and its variables),
  2. Demographics Data of the patients
  3. Independent Variables EDA: Slicing the Data for an Overview
  4. Independent Variables EDA: Boxplots, Scatterplots, ANOVA, & Chi-Square
  5. Linear Regression Model
  6. Conclusion

2 Chapter 2: Description of the Data

2.1 Source Data

The data looks like the following:

tibble [3,144 × 82] (S3: tbl_df/tbl/data.frame)
 $ Province                                                                                               : chr [1:3144] "New York City" "Nassau" "Suffolk" "Westchester" ...
 $ State                                                                                                  : chr [1:3144] "New York" "New York" "New York" "New York" ...
 $ Latitude                                                                                               : num [1:3144] 40.8 40.7 40.9 41.2 41.8 ...
 $ Longitude                                                                                              : num [1:3144] -74 -73.6 -72.8 -73.8 -87.8 ...
 $ Tests                                                                                                  : num [1:3144] 499143 499143 499143 499143 110616 ...
 $ Days Since 1st Case                                                                                    : num [1:3144] 44 41 38 43 82 35 41 80 39 38 ...
 $ total_cases                                                                                            : num [1:3144] 110465 25250 22691 20191 16323 ...
 $ deaths                                                                                                 : num [1:3144] 7905 1001 608 596 577 ...
 $ Population (for demographic %'s)                                                                       : chr [1:3144] "8623000" "1358343" "1481093" "967612" ...
 $ % less than 18 years of age                                                                            : chr [1:3144] "20.9" "21.459675499999999" "21.134324500000002" "21.900513799999999" ...
 $ % 65 and over                                                                                          : chr [1:3144] "14.1" "17.763039200000001" "16.862951899999999" "17.053116299999999" ...
 $ % Black                                                                                                : chr [1:3144] "24.3" "11.6331442" "7.3924459799999998" "13.8042935" ...
 $ % American Indian & Alaska Native                                                                      : chr [1:3144] "0.4" "0.54294092000000005" "0.61373593999999998" "0.95647842000000005" ...
 $ % Asian                                                                                                : chr [1:3144] "13.9" "10.4504532" "4.1896086199999996" "6.43553408" ...
 $ % Native Hawaiian/Other Pacific Islander                                                               : chr [1:3144] "0.1" "0.1" "9.5899999999999999E-2" "0.13228443000000001" ...
 $ % Hispanic                                                                                             : chr [1:3144] "29.1" "17.231362000000001" "19.775260599999999" "25.140345499999999" ...
 $ % Non-Hispanic White                                                                                   : chr [1:3144] "32.1" "59.333835399999998" "67.190378999999993" "53.088118000000001" ...
 $ % Not Proficient in English                                                                            : chr [1:3144] "9" "5.3660427200000003" "4.00639637" "6.3180527499999997" ...
 $ % Female                                                                                               : chr [1:3144] "52.3" "51.306334300000003" "50.771693599999999" "51.559095999999997" ...
 $ % Rural                                                                                                : chr [1:3144] "0" "0.19223132000000001" "2.6011316799999999" "3.2734774500000001" ...
 $ Population Density per Square mile of Land (2010)                                                      : num [1:3144] 69468 4705 1637 2205 5495 ...
 $ Housing Density Per Square Mile of Land                                                                : num [1:3144] 37106 1645 625 861 2306 ...
 $ Avg Daily March 2011 Sunlight (KJ/m²) Missing HI and AK                                                : num [1:3144] 16233 16649 16539 15031 14299 ...
 $ GDP 2018                                                                                               : num [1:3144] 600244287 81196003 81211899 73404644 362063569 ...
 $ GDP/capita                                                                                             : num [1:3144] 69.6 59.8 54.8 75.9 69.9 ...
 $ Percentage Living in Poverty, All Ages, 2016                                                           : num [1:3144] 17.2 6.1 7.6 10 15 22.9 6.9 16.3 14.4 15.6 ...
 $ Air Quality, Annual Average Ambient Concentrations of PM2.5, 2014                                      : chr [1:3144] "10.8" "10" "9" "10.4" ...
 $ Primary Care Physicians Ratio                                                                          : chr [1:3144] "31.417361111111109" "29.834027777777777" "56.709027777777777" "30.334027777777777" ...
 $ Dentist Ratio                                                                                          : chr [1:3144] "23.334027777777777" "34.500694444444441" "50.209027777777777" "37.834027777777777" ...
 $ Mental Health Provider Ratio                                                                           : chr [1:3144] "4.834027777777778" "13.792361111111111" "15.625694444444443" "10.750694444444443" ...
 $ High School Graduation Rate                                                                            : chr [1:3144] "74.536495200000005" "90.769602500000005" "89.560601599999998" "89.554779199999999" ...
 $ % Some College                                                                                         : chr [1:3144] "84.074597800000006" "75.579882699999999" "67.067068199999994" "71.893479799999994" ...
 $ % Unemployed                                                                                           : chr [1:3144] "3.6720665100000001" "3.5355112100000001" "3.8509406199999998" "3.8880261699999998" ...
 $ % Children in Poverty                                                                                  : chr [1:3144] "19.7" "7.6" "9.4" "10.3" ...
 $ Income Inequality Ratio (80th%/20th%)                                                                  : chr [1:3144] "9.2065919600000008" "4.5137498100000002" "4.3752126000000002" "6.18534249" ...
 $ % Single-Parent Households                                                                             : chr [1:3144] "39.575203500000001" "19.238140600000001" "23.6102569" "25.424638399999999" ...
 $ Social Association Rate                                                                                : chr [1:3144] "12.8789886" "7.9882352399999998" "6.7450214400000004" "8.3754656999999995" ...
 $ Violent Crime Rate                                                                                     : chr [1:3144] "586.40744800000004" "143.663387" "124.039181" "220.606166" ...
 $ Air pollution: Average Daily PM2.5                                                                     : chr [1:3144] "10.8" "10" "9" "10.4" ...
 $ Presence of Drinking Water Violation                                                                   : chr [1:3144] "No" "No" "No" "Yes" ...
 $ % Severe Housing Problems                                                                              : chr [1:3144] "24.378637699999999" "21.324080599999998" "22.888761800000001" "24.236306200000001" ...
 $ Housing: Severe Cost Burden                                                                            : chr [1:3144] "19.610767299999999" "19.1674103" "20.427237699999999" "20.895964899999999" ...
 $ Housing: Overcrowding                                                                                  : chr [1:3144] "5.4547143900000004" "2.5236808000000002" "2.6421104199999998" "4.2602996299999996" ...
 $ Housing: Inadequate Facilities                                                                         : chr [1:3144] "1.2204915199999999" "0.72802853000000001" "0.78609931" "0.73443351999999995" ...
 $ % Drive Alone to Work                                                                                  : chr [1:3144] "6.0475223400000004" "68.609857500000004" "79.604339800000005" "57.587820100000002" ...
 $ % Long Commute - Drives Alone                                                                          : chr [1:3144] "66.7" "45.7" "41.9" "41.2" ...
 $ Sleep <7 Hours_Percent                                                                                 : chr [1:3144] "NA" "38.049835600000002" "35.608102700000003" "33.101763800000001" ...
 $ Sleep <7 Hours_CI_Low                                                                                  : chr [1:3144] "NA" "37.488497199999998" "34.960704200000002" "32.608553999999998" ...
 $ Sleep <7 Hours_CI_High                                                                                 : chr [1:3144] "NA" "38.576512200000003" "36.198949800000001" "33.594731299999999" ...
 $ Diabetes Total Percentage                                                                              : num [1:3144] 6.5 7.2 6.8 6.4 9 10.3 6.8 8.1 6.9 8.2 ...
 $ Diabetes Male Percentage                                                                               : num [1:3144] 6.7 8.5 7.7 6.7 9.7 10.7 7.1 8.6 7 8.7 ...
 $ Diabetes Female Percentage                                                                             : num [1:3144] 6.3 6.2 6 6.2 8.4 10.1 6.6 7.7 6.8 7.9 ...
 $ Coronary Heart Disease Death Rate per 100,000, All Ages, All Races/Ethnicities, Both Genders, 2014-2016: num [1:3144] 100.4 142.4 120.1 97.6 95.2 ...
 $ Hypertension Death Rate per 100,000 (any mention), 35+, All Races/Ethnicities, Both Genders, 2014-2016 : num [1:3144] 232 153 181 124 191 ...
 $ Obesity, Age-Adjusted Percentage, 20+. 2015                                                            : num [1:3144] 15.9 22.5 23.6 20.2 27.2 34.1 22.4 21.2 23.4 23.9 ...
 $ % Fair or Poor Health                                                                                  : chr [1:3144] "15.610279800000001" "12.0544118" "13.0711332" "14.8011888" ...
 $ Average Number of Physically Unhealthy Days                                                            : chr [1:3144] "3.5938226700000002" "2.8691053700000002" "3.1473144999999998" "3.1513169799999998" ...
 $ Average Number of Mentally Unhealthy Days                                                              : chr [1:3144] "3.97126146" "3.4601849699999998" "3.9316660200000002" "3.9107989299999999" ...
 $ % Low Birthweight                                                                                      : chr [1:3144] "8.2870096600000007" "7.8873580399999996" "7.7408509700000003" "7.95359718" ...
 $ % Smokers (adults)                                                                                     : chr [1:3144] "12.418234200000001" "11.225364600000001" "12.625481499999999" "11.371546" ...
 $ % Adults with Obesity                                                                                  : chr [1:3144] "14.6" "23.6" "24.6" "20.7" ...
 $ Food Environment Index                                                                                 : chr [1:3144] "8.3000000000000007" "9.6999999999999993" "9.3000000000000007" "9.1" ...
 $ % Physically Inactive                                                                                  : chr [1:3144] "17.5" "22.8" "24.2" "21.2" ...
 $ % With Access to Exercise Opportunities                                                                : chr [1:3144] "100" "98.858183299999993" "93.3366592" "99.621119899999997" ...
 $ % Excessive Drinking                                                                                   : chr [1:3144] "24.812851999999999" "18.439903699999999" "18.671426799999999" "18.011370899999999" ...
 $ % Uninsured                                                                                            : chr [1:3144] "6.15572813" "5.32768102" "5.4469207300000004" "6.9293390300000004" ...
 $ Preventable Hospitalization Rate (Preventable hospital stays)                                          : chr [1:3144] "3082" "3588" "4339" "3870" ...
 $ % With Annual Mammogram                                                                                : chr [1:3144] "39" "45" "42" "46" ...
 $ % Flu Vaccinated                                                                                       : chr [1:3144] "46" "52" "51" "51" ...
 $ Chronic Respiratory Disease: mortality rate per 100K (2014)                                            : chr [1:3144] "23.47" "29.03" "38.590000000000003" "31.82" ...
 $ Liver Disease: crude mortality rate per 100K (1999-2018)                                               : chr [1:3144] "7.3202151400000002" "7.8321364999999998" "9.3999156199999998" "8.6888457900000002" ...
 $ Liver Disease: % of Total Deaths (1999-2018)                                                           : chr [1:3144] "2.72836E-3" "2.4593800000000002E-3" "3.2464299999999998E-3" "1.92728E-3" ...
 $ Liver Disease: crude mortality rate per 100K (2018)                                                    : chr [1:3144] "6.6924499900000001" "8.7606738499999999" "11.478009800000001" "8.9912072199999997" ...
 $ Liver Disease: % of Total Deaths (2018)                                                                : chr [1:3144] "1.9492800000000001E-3" "2.1281199999999998E-3" "3.04017E-3" "1.5558499999999999E-3" ...
 $ Avg Temp Peak Growth-10 Rate                                                                           : num [1:3144] 8.41 7.41 6.86 5.88 2.25 ...
 $ Avg Temp 10 Before First-Current                                                                       : num [1:3144] 8.33 7.78 7.1 6.68 2.55 ...
 $ Avg Temp First-Current                                                                                 : num [1:3144] 9.23 8.36 7.82 7.53 3.34 ...
 $ First Case                                                                                             : POSIXct[1:3144], format: "2020-03-02" "2020-03-05" ...
 $ Stay At Home                                                                                           : POSIXct[1:3144], format: "2020-03-22" "2020-03-22" ...
 $ No Cases                                                                                               : num [1:3144] 0 0 0 0 0 0 0 0 0 0 ...
 $ No Stay At Home Order                                                                                  : num [1:3144] 0 0 0 0 0 0 0 0 0 0 ...
 $ Stay At Home Order After First Case                                                                    : num [1:3144] 1 1 1 1 1 1 1 1 1 1 ...
# A tibble: 3,144 x 16
   Province State total_cases deaths `Population (fo… `% less than 18…
   <chr>    <chr>       <dbl>  <dbl> <chr>            <chr>           
 1 New Yor… New …      110465   7905 8623000          20.9            
 2 Nassau   New …       25250   1001 1358343          21.459675499999…
 3 Suffolk  New …       22691    608 1481093          21.134324500000…
 4 Westche… New …       20191    596 967612           21.900513799999…
 5 Cook     Illi…       16323    577 5180493          21.8062644      
 6 Wayne    Mich…       12209    820 1753893          23.617233200000…
 7 Bergen   New …       10426    550 936692           21.176117699999…
 8 Los Ang… Cali…       10047    360 10105518         21.660374099999…
 9 Rockland New …        8335    263 325695           28.158860300000…
10 Hudson   New …        8242    277 676061           20.4450486      
# … with 3,134 more rows, and 10 more variables: `% 65 and over` <chr>, `%
#   Black` <chr>, `% American Indian & Alaska Native` <chr>, `% Asian` <chr>,
#   `% Native Hawaiian/Other Pacific Islander` <chr>, `% Hispanic` <chr>, `%
#   Non-Hispanic White` <chr>, `% Not Proficient in English` <chr>, `%
#   Female` <chr>, poor_health <chr>

The Covid19 dataset has 82 columns and 3144 rows/entries, for a total of 257808 individual data points. Out of 82, we select the following variables to do EDA:

  1. Province
  2. State
  3. State Code
  4. Tests
  5. Total cases
  6. Deaths
  7. Population (for demographic %’s)
  8. % less than 18 years of age
  9. % 65 and over
  10. % Black
  11. % American Indian & Alaska Native
  12. % Asian
  13. % Native Hawaiian/Other Pacific Islander
  14. % Hispanic
  15. % Non-Hispanic White
  16. % Not Proficient in English
  17. % Female
  18. No Cases
  19. No Stay At Home Order
  20. Stay At Home Order After First Case
  21. Percentage Living in Poverty
  22. Social Association Rate

To prepare our data for EDA we clean the dataset and remove all NAs.

tibble [3,144 × 15] (S3: tbl_df/tbl/data.frame)
 $ Province                                : chr [1:3144] "New York City" "Nassau" "Suffolk" "Westchester" ...
 $ State                                   : chr [1:3144] "New York" "New York" "New York" "New York" ...
 $ total_cases                             : num [1:3144] 110465 25250 22691 20191 16323 ...
 $ deaths                                  : num [1:3144] 7905 1001 608 596 577 ...
 $ Population (for demographic %'s)        : chr [1:3144] "8623000" "1358343" "1481093" "967612" ...
 $ % less than 18 years of age             : chr [1:3144] "20.9" "21.459675499999999" "21.134324500000002" "21.900513799999999" ...
 $ % 65 and over                           : chr [1:3144] "14.1" "17.763039200000001" "16.862951899999999" "17.053116299999999" ...
 $ % Black                                 : chr [1:3144] "24.3" "11.6331442" "7.3924459799999998" "13.8042935" ...
 $ % American Indian & Alaska Native       : chr [1:3144] "0.4" "0.54294092000000005" "0.61373593999999998" "0.95647842000000005" ...
 $ % Asian                                 : chr [1:3144] "13.9" "10.4504532" "4.1896086199999996" "6.43553408" ...
 $ % Native Hawaiian/Other Pacific Islander: chr [1:3144] "0.1" "0.1" "9.5899999999999999E-2" "0.13228443000000001" ...
 $ % Hispanic                              : chr [1:3144] "29.1" "17.231362000000001" "19.775260599999999" "25.140345499999999" ...
 $ % Non-Hispanic White                    : chr [1:3144] "32.1" "59.333835399999998" "67.190378999999993" "53.088118000000001" ...
 $ % Not Proficient in English             : chr [1:3144] "9" "5.3660427200000003" "4.00639637" "6.3180527499999997" ...
 $ % Female                                : chr [1:3144] "52.3" "51.306334300000003" "50.771693599999999" "51.559095999999997" ...

3 Chapter 3: Independent Variables EDA

3.1 United States COVID-19 Cases and Deaths by Provinces (Cities)

3.1.1 What are the top 15 Provinces based on the number of cases?

The following bar chart shows the top 15 cities by number of Covid19 cases.

3.1.2 What are the top 15 Provinces based on the number of deaths?

The following bar chart shows the top 15 cities by number of deaths.

3.1.3 What is the average cases for each State?

                  State total_cases
1               Alabama       59.03
2                Alaska        9.83
3               Arizona      258.60
4              Arkansas       19.44
5            California      437.43
6              Colorado      122.41
7           Connecticut     1682.00
8              Delaware      638.33
9  District of Columbia     2058.00
10              Florida      323.07
11              Georgia       85.74
12               Hawaii      101.60
13                Idaho       33.32
14             Illinois      227.48
15              Indiana       94.12
16                 Iowa       19.20
17               Kansas       13.84
18             Kentucky       17.32
19            Louisiana      335.34
20                Maine       45.88
21             Maryland      394.75
22        Massachusetts     1843.87
23             Michigan      316.96
24            Minnesota       19.10
25          Mississippi       37.68
26             Missouri       40.98
27              Montana        7.18
28             Nebraska        9.48
29               Nevada      184.35
30        New Hampshire      103.50
31           New Jersey     3196.29
32           New Mexico       40.82
33             New York     3274.52
34       North Carolina       51.20
35         North Dakota        6.45
36                 Ohio       82.81
37             Oklahoma       28.51
 [ reached 'max' / getOption("max.print") -- omitted 14 rows ]

3.1.4 What is the average deaths for each State?

                  State  deaths
1               Alabama   1.701
2                Alaska   0.172
3               Arizona   7.133
4              Arkansas   0.427
5            California  13.328
6              Colorado   5.109
7           Connecticut  83.375
8              Delaware  14.333
9  District of Columbia  67.000
10              Florida   7.836
11              Georgia   3.270
12               Hawaii   1.800
13                Idaho   0.750
14             Illinois   8.510
15              Indiana   4.207
16                 Iowa   0.444
17               Kansas   0.657
18             Kentucky   0.900
19            Louisiana  15.922
20                Maine   1.250
21             Maryland  12.667
22        Massachusetts  49.600
23             Michigan  21.133
24            Minnesota   0.920
25          Mississippi   1.366
26             Missouri   1.284
27              Montana   0.143
28             Nebraska   0.161
29               Nevada   7.059
30        New Hampshire   0.300
31           New Jersey 133.476
32           New Mexico   0.939
33             New York 174.871
34       North Carolina   1.130
35         North Dakota   0.151
36                 Ohio   3.705
37             Oklahoma   1.403
 [ reached 'max' / getOption("max.print") -- omitted 14 rows ]

3.1.5 Which cities had the greatest % of population of people with poor health?

3.2 Patient Demographics

3.2.1 What are the patient demographics?

[1] "D:/study/6101/repo/Data_Science"
Table: Statistics summary.
TC Population young old black AIAN Asian NH Hispanic NHW Female Poverty Social
Min 0 88 0.0 4.8 0.0 0.0 0.0 0.0 0.6 2.7 26.8 3.4 0.0
Q1 2 11034 20.1 16.3 0.7 0.4 0.5 0.0 2.4 64.7 49.4 11.4 8.2
Median 9 25758 22.1 19.0 2.2 0.6 0.7 0.1 4.4 83.5 50.3 14.8 11.1
Mean 191 105871 22.1 19.3 8.8 2.4 1.5 0.1 9.6 76.2 49.9 15.9 11.6
Q3 39 67013 23.8 21.8 9.6 1.3 1.4 0.1 9.9 92.3 51.0 19.0 14.4
Max 110465 10105518 42.0 57.6 85.4 92.5 43.4 48.9 96.4 97.9 56.9 48.6 52.3

From the average of the output results, we can see that the average proportion of teenagers under the age of 18 is 22.1%, and the average proportion of people over 65 is 19.3%. The largest number of all races is Non-Hispanic White, with an average proportion of 76.2. The average proportion of women is 49.9, the average proportion of the poor is 15.9%, and the average of the Social Association Rate is 11.6. We divide the data into four levels according to total cases.

3.2.2 Which race is the majority of the sample?

According to the average value, we get a pie chart of race proportions, from which we can see the overall proportions of different races. In the following, we will study the proportion of which race is related to the number of confirmed cases and the number of deaths.

3.3 Stay at home policy in each province

3.4 Underlying Health Conditions

3.4.1 Are there any common underlying health conditions?

3.5 Impact of Temperature

4 Chapter 4: Independent Variables EDA: Boxplots, Scatterplots, ANOVA, & Chi-Square

[1]      0      2      9     39 110465

    Shapiro-Wilk normality test

data:  df2$TC
W = 0.05, p-value <0.0000000000000002

    Bartlett test of homogeneity of variances

data:  TC by rank
Bartlett's K-squared = 25341, df = 3, p-value <0.0000000000000002

The Shapiro-Wilk test is used to test whether the data conforms to the normal distribution. H0: The sample data is not significantly different from the normal distribution H1: The sample data is significantly different from the normal distribution The p-value is less than 0.05, the null hypothesis is rejected, and the total cases do not conform to the normal distribution.

Test for homogeneity of variance H0: The variances of the groups are not significantly different H1: The variances of several groups are significantly different The result shows that the p value is less than 0.05, rejecting the null hypothesis, and total cases do not meet the homogeneity of variance.

5 Chapter 5: Linear Regression Model

5.1 SMART Question: What factors influence the death the most?

6 Chapter 6: Conclusion

7 Chapter 7: Bibliography

Cases in the U.S. (2020, August 01). Retrieved August 01, 2020, from https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html